Image("E:\DataScience\Data_Center\T_20_World_cup_data\ICC_Men's_T20_World_Cup_2021.png")
pwd
'E:\\DataScience\\Data_Center\\T_20_World_cup_data'
import os
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
import plotly.express as px
import pandas as pd
import numpy as np
from scipy import signal
#to supress warning
import warnings
warnings.filterwarnings('ignore')
#to make shell more intractive
from IPython.display import display
from IPython.display import Image
# setting up the chart size and background
plt.rcParams['figure.figsize'] = (16, 8)
plt.style.use('fivethirtyeight')
path ="E:\DataScience\Data_Center\T_20_World_cup_data"
dir_list = os.listdir(path)
print(dir_list)
['.ipynb_checkpoints', 'EDA_T-20WorldCup.ipynb', "ICC_Men's_T20_World_Cup_2021.png", 'kaggle_data.csv']
df =pd.read_csv("E:\DataScience\Data_Center\T_20_World_cup_data\kaggle_data.csv")
df.head()
| Unnamed: 0 | team_1 | team_2 | stage | Winner_toss | Toss_descision | time | venue | avg_temperature | best_bowler | ... | best_bowler_country | best_batter | batting_hand | high_indvidual_scores | strike_rate | best_batter_team | target | target_achieved | Player_of_the_match | Winner | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Match_1 | Australia | SouthAfrica | Group_stage | Australia | Fielding | afternoon | Abu_Dhabi | 30 | Josh_Hazlewood | ... | Australia | Aiden_Markram | Right | 40 | 111.10 | SouthAfrica | 119 | 1 | Josh_Hazlewood | Australia |
| 1 | Match_2 | England | Windies | Group_stage | England | Fielding | evening | Dubai | 33 | Adil_Rashid | ... | England | Jos_Buttler | Right | 24 | 109.10 | England | 56 | 1 | Moeen_Ali | England |
| 2 | Match_3 | Srilanka | Bangladesh | Group_stage | Srilanka | Fielding | afternoon | Sharjah | 34 | Shakib_al_Hassan | ... | Bangladesh | Charith_Asalanka | Left | 80 | 163.20 | Srilanka | 172 | 1 | Charith_Asalanka | Srilanka |
| 3 | Match_4 | Pakistan | India | Group_stage | Pakistan | Fielding | evening | Dubai | 34 | Shaheen_shah | ... | Pakistan | Muhammad_Rizwan | Right | 79 | 143.60 | Pakistan | 152 | 1 | Shaheen_shah | Pakistan |
| 4 | Match_5 | Afghanistan | Scotland | Group_stage | Afghanistan | Batting | evening | Sharjah | 33 | Mujeeb_ur_Rehman | ... | Afghanistan | Najibullah_Zadran | Left | 59 | 173.53 | Afghanistan | 191 | 0 | Mujeeb_ur_Rehman | Afghanistan |
5 rows × 24 columns
df.tail()
| Unnamed: 0 | team_1 | team_2 | stage | Winner_toss | Toss_descision | time | venue | avg_temperature | best_bowler | ... | best_bowler_country | best_batter | batting_hand | high_indvidual_scores | strike_rate | best_batter_team | target | target_achieved | Player_of_the_match | Winner | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | Match_29 | Pakistan | ScotLand | Group_stage | Pakistan | Batting | evening | Sharjah | 27 | Shadab_Khan | ... | Pakistan | Babar_Azam | Right | 66 | 140.43 | Pakistan | 190 | 0 | Shoaib_Malik | Pakistan |
| 29 | Match_30 | India | Namibia | Group_stage | India | Fielding | evening | Dubai | 28 | Ravindra_Jadeja | ... | India | Rohit_Sharma | Right | 56 | 151.35 | India | 133 | 1 | Ravindra_Jadeja | India |
| 30 | Match_31 | New_Zealand | England | Semi_Final | New_Zealand | Fielding | evening | Abu_Dhabi | 28 | Liam_Livingstone | ... | England | Daryl_Mitchell | Right | 72 | 153.19 | New_Zealand | 167 | 1 | Daryl_Mitchell | New_Zealand |
| 31 | Match_32 | Australia | Pakistan | Semi_Final | Australia | Fielding | evening | Dubai | 29 | Shadab_Khan | ... | Pakistan | Muhammad_Rizwan | Right | 67 | 128.85 | Pakistan | 177 | 1 | Matthew_Wade | Australia |
| 32 | Match_33 | Australia | New_Zealand | Final | Australia | Fielding | evening | Dubai | 26 | Josh_Hazlewood | ... | Australia | Kane_Williamson | Right | 85 | 177.08 | New_Zealand | 173 | 1 | Mitchell_Marsh | Australia |
5 rows × 24 columns
df.columns
Index(['Unnamed: 0', 'team_1', 'team_2', 'stage', 'Winner_toss',
'Toss_descision', 'time', 'venue', 'avg_temperature', 'best_bowler',
'bowling_arm', 'bowling_style', 'most_individual_wickets', 'economy',
'best_bowler_country', 'best_batter', 'batting_hand',
'high_indvidual_scores', 'strike_rate', 'best_batter_team', 'target',
'target_achieved', 'Player_of_the_match', 'Winner'],
dtype='object')
df.describe()
| avg_temperature | most_individual_wickets | economy | high_indvidual_scores | strike_rate | target | target_achieved | |
|---|---|---|---|---|---|---|---|
| count | 33.000000 | 33.000000 | 33.000000 | 33.000000 | 33.000000 | 33.000000 | 33.000000 |
| mean | 29.272727 | 2.878788 | 5.215758 | 62.393939 | 152.402121 | 146.606061 | 0.696970 |
| std | 2.577217 | 0.892944 | 2.187966 | 20.135385 | 33.999070 | 37.136521 | 0.466694 |
| min | 20.000000 | 1.000000 | 0.900000 | 24.000000 | 97.060000 | 56.000000 | 0.000000 |
| 25% | 28.000000 | 2.000000 | 4.000000 | 45.000000 | 136.360000 | 125.000000 | 0.000000 |
| 50% | 29.000000 | 3.000000 | 4.800000 | 65.000000 | 153.190000 | 152.000000 | 1.000000 |
| 75% | 30.000000 | 3.000000 | 6.250000 | 79.000000 | 162.900000 | 173.000000 | 1.000000 |
| max | 34.000000 | 5.000000 | 12.000000 | 101.000000 | 263.160000 | 211.000000 | 1.000000 |
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 33 entries, 0 to 32 Data columns (total 24 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Unnamed: 0 33 non-null object 1 team_1 33 non-null object 2 team_2 33 non-null object 3 stage 33 non-null object 4 Winner_toss 33 non-null object 5 Toss_descision 33 non-null object 6 time 33 non-null object 7 venue 33 non-null object 8 avg_temperature 33 non-null int64 9 best_bowler 33 non-null object 10 bowling_arm 33 non-null object 11 bowling_style 33 non-null object 12 most_individual_wickets 33 non-null int64 13 economy 33 non-null float64 14 best_bowler_country 33 non-null object 15 best_batter 33 non-null object 16 batting_hand 33 non-null object 17 high_indvidual_scores 33 non-null int64 18 strike_rate 33 non-null float64 19 best_batter_team 33 non-null object 20 target 33 non-null int64 21 target_achieved 33 non-null int64 22 Player_of_the_match 33 non-null object 23 Winner 33 non-null object dtypes: float64(2), int64(5), object(17) memory usage: 6.3+ KB
df.isnull().sum()
Unnamed: 0 0 team_1 0 team_2 0 stage 0 Winner_toss 0 Toss_descision 0 time 0 venue 0 avg_temperature 0 best_bowler 0 bowling_arm 0 bowling_style 0 most_individual_wickets 0 economy 0 best_bowler_country 0 best_batter 0 batting_hand 0 high_indvidual_scores 0 strike_rate 0 best_batter_team 0 target 0 target_achieved 0 Player_of_the_match 0 Winner 0 dtype: int64
df.nunique(axis=0)
Unnamed: 0 33 team_1 10 team_2 13 stage 3 Winner_toss 11 Toss_descision 2 time 2 venue 3 avg_temperature 9 best_bowler 26 bowling_arm 2 bowling_style 5 most_individual_wickets 5 economy 24 best_bowler_country 11 best_batter 23 batting_hand 2 high_indvidual_scores 28 strike_rate 33 best_batter_team 11 target 27 target_achieved 2 Player_of_the_match 30 Winner 10 dtype: int64
A=df['team_1'].unique()
print(A)
['Australia' 'England' 'Srilanka' 'Pakistan' 'Afghanistan' 'SouthAfrica' 'Namibia' 'Windies' 'New_Zealand' 'India']
B =df['stage'].unique()
print(B)
['Group_stage' 'Semi_Final' 'Final']
C=df['Winner_toss'].unique()
print(C)
['Australia' 'England' 'Srilanka' 'Pakistan' 'Afghanistan' 'SouthAfrica' 'Bangladesh' 'Namibia' 'New_Zealand' 'Windies' 'India']
D=df['Toss_descision'].unique()
print(D)
['Fielding' 'Batting']
E=df['time'].unique()
print(E)
['afternoon' 'evening']
F =df['venue'].unique()
print(F)
['Abu_Dhabi' 'Dubai' 'Sharjah']
G=df['avg_temperature'].unique()
print(G)
[30 33 34 29 28 31 20 27 26]
I =df['best_bowler'].unique()
print(I)
['Josh_Hazlewood' 'Adil_Rashid' 'Shakib_al_Hassan' 'Shaheen_shah' 'Mujeeb_ur_Rehman' 'Dwaine_Pretorious' 'Haris_Rauf' 'Tymal_Mills' 'Ruben_Trumpelmann' 'Adam_Zampa' 'Shoriful_Islam' 'Imad_Wasim' 'Tabraiz_Shamsi' 'Criss_Jordan' 'Hamid_Hassan' 'Trent_Boult' 'Wanindu_Hasaranga' 'Anrich_Nortje' 'Imad_wasim' 'Safyaan_Sharif' 'Mohammed Shami' 'Tim_Southee' 'Ravindra_Jadeja' 'Kagiso_Rabada' 'Shadab_Khan' 'Liam_Livingstone']
H =df['bowling_arm'].unique()
print(H)
['Right' 'Left']
J =df['bowling_style'].unique()
print(J)
['Pacer' 'Leg_spin' 'Off_spin' 'Orthodox' 'Unorthodox']
K =df['most_individual_wickets'].unique()
print(K)
[2 4 3 5 1]
H =df['best_batter_team'].unique()
print(H)
['SouthAfrica' 'England' 'Srilanka' 'Pakistan' 'Afghanistan' 'Windies' 'ScotLand' 'Australia' 'Bangladesh' 'New_Zealand' 'India']
I =df['Player_of_the_match'].unique()
print(I)
['Josh_Hazlewood' 'Moeen_Ali' 'Charith_Asalanka' 'Shaheen_shah' 'Mujeeb_ur_Rehman' 'Anrich_Nortje' 'Haris_Rauf' 'Jason_Roy' 'Ruben_Trumpelmann' 'Adam_Zampa' 'Nicholas_Pooran' 'Asif_Ali' 'Tabraiz_Shamsi' 'Chris_Jordan' 'Naveen_ul_Haq' 'Trent_Boult' 'Jos_Buttler' 'Kagiso_Rabada' 'Muhammad_Rizwan' 'Martin_Guptill' 'Rohit_Sharma' 'Chrith_Adalanka' 'James_Neesham' 'Ravindra_Jadeja' 'David_Warner' 'Rassie_van_der_Dussen' 'Shoaib_Malik' 'Daryl_Mitchell' 'Matthew_Wade' 'Mitchell_Marsh']
#remnaming
df = df.rename(columns = {'Unnamed: 0' : 'Match Number'})
df.head()
| Match Number | team_1 | team_2 | stage | Winner_toss | Toss_descision | time | venue | avg_temperature | best_bowler | ... | best_bowler_country | best_batter | batting_hand | high_indvidual_scores | strike_rate | best_batter_team | target | target_achieved | Player_of_the_match | Winner | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Match_1 | Australia | SouthAfrica | Group_stage | Australia | Fielding | afternoon | Abu_Dhabi | 30 | Josh_Hazlewood | ... | Australia | Aiden_Markram | Right | 40 | 111.10 | SouthAfrica | 119 | 1 | Josh_Hazlewood | Australia |
| 1 | Match_2 | England | Windies | Group_stage | England | Fielding | evening | Dubai | 33 | Adil_Rashid | ... | England | Jos_Buttler | Right | 24 | 109.10 | England | 56 | 1 | Moeen_Ali | England |
| 2 | Match_3 | Srilanka | Bangladesh | Group_stage | Srilanka | Fielding | afternoon | Sharjah | 34 | Shakib_al_Hassan | ... | Bangladesh | Charith_Asalanka | Left | 80 | 163.20 | Srilanka | 172 | 1 | Charith_Asalanka | Srilanka |
| 3 | Match_4 | Pakistan | India | Group_stage | Pakistan | Fielding | evening | Dubai | 34 | Shaheen_shah | ... | Pakistan | Muhammad_Rizwan | Right | 79 | 143.60 | Pakistan | 152 | 1 | Shaheen_shah | Pakistan |
| 4 | Match_5 | Afghanistan | Scotland | Group_stage | Afghanistan | Batting | evening | Sharjah | 33 | Mujeeb_ur_Rehman | ... | Afghanistan | Najibullah_Zadran | Left | 59 | 173.53 | Afghanistan | 191 | 0 | Mujeeb_ur_Rehman | Afghanistan |
5 rows × 24 columns
#index match
df = df.set_index('Match Number')
df.head()
| team_1 | team_2 | stage | Winner_toss | Toss_descision | time | venue | avg_temperature | best_bowler | bowling_arm | ... | best_bowler_country | best_batter | batting_hand | high_indvidual_scores | strike_rate | best_batter_team | target | target_achieved | Player_of_the_match | Winner | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Match Number | |||||||||||||||||||||
| Match_1 | Australia | SouthAfrica | Group_stage | Australia | Fielding | afternoon | Abu_Dhabi | 30 | Josh_Hazlewood | Right | ... | Australia | Aiden_Markram | Right | 40 | 111.10 | SouthAfrica | 119 | 1 | Josh_Hazlewood | Australia |
| Match_2 | England | Windies | Group_stage | England | Fielding | evening | Dubai | 33 | Adil_Rashid | Right | ... | England | Jos_Buttler | Right | 24 | 109.10 | England | 56 | 1 | Moeen_Ali | England |
| Match_3 | Srilanka | Bangladesh | Group_stage | Srilanka | Fielding | afternoon | Sharjah | 34 | Shakib_al_Hassan | Left | ... | Bangladesh | Charith_Asalanka | Left | 80 | 163.20 | Srilanka | 172 | 1 | Charith_Asalanka | Srilanka |
| Match_4 | Pakistan | India | Group_stage | Pakistan | Fielding | evening | Dubai | 34 | Shaheen_shah | Left | ... | Pakistan | Muhammad_Rizwan | Right | 79 | 143.60 | Pakistan | 152 | 1 | Shaheen_shah | Pakistan |
| Match_5 | Afghanistan | Scotland | Group_stage | Afghanistan | Batting | evening | Sharjah | 33 | Mujeeb_ur_Rehman | Right | ... | Afghanistan | Najibullah_Zadran | Left | 59 | 173.53 | Afghanistan | 191 | 0 | Mujeeb_ur_Rehman | Afghanistan |
5 rows × 23 columns
Time =df['time'].value_counts()
Time
evening 20 afternoon 13 Name: time, dtype: int64
type(Time)
pandas.core.series.Series
toss_list =df['Winner_toss'].tolist()
win_list=df['Winner'].tolist()
winner=0
looser=0
for i in range(len(toss_list)):
if(toss_list[i] == win_list[i]):
winner +=1
else:
looser +=1
print("Won Toss and Won Match:",winner)
print("Won Toss and Loose Match:",looser)
Won Toss and Won Match: 24 Won Toss and Loose Match: 9
#match won by each team
plt.figure(figsize = (8,6))
sns.countplot(df['time'], palette = 'Set1')
plt.title("Time Slot :Match")
plt.show()
# Temprature range:
fig =px.pie(Time ,values =df['avg_temperature'].value_counts(),names=['30-Temp','33-Temp','34-Temp','29-Temp',
'28-Temp','31-Temp','20-Temp','27-temp','26-Temp'],
title=' Temparature Range Records while Match:')
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.show()
#temp in *c
#match won by each team
sns.countplot(y='Winner',data=df)
<AxesSubplot:xlabel='count', ylabel='Winner'>
#count of venue
plt.figure(figsize = (8,6))
sns.countplot(df['venue'], palette = 'Set1')
plt.title("Venue for Tournament")
plt.show()
#Man of MATCH
print("Player : No_of_times_player_of_match")
df['Player_of_the_match'].value_counts().nlargest(5)
Player : No_of_times_player_of_match
Ravindra_Jadeja 2 Adam_Zampa 2 Trent_Boult 2 Kagiso_Rabada 1 Muhammad_Rizwan 1 Name: Player_of_the_match, dtype: int64
# correlation
plt.figure(figsize = (8,6))
sns.heatmap(df.corr(), annot = True, cmap = 'OrRd')
plt.title("Correlation")
plt.show()
#colum name-update
df.target_achieved[df['target_achieved'] == 0] = 'Achieved'
df.target_achieved[df['target_achieved'] == 1] = 'Not Achieved'
df.head()
| team_1 | team_2 | stage | Winner_toss | Toss_descision | time | venue | avg_temperature | best_bowler | bowling_arm | ... | best_bowler_country | best_batter | batting_hand | high_indvidual_scores | strike_rate | best_batter_team | target | target_achieved | Player_of_the_match | Winner | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Match Number | |||||||||||||||||||||
| Match_1 | Australia | SouthAfrica | Group_stage | Australia | Fielding | afternoon | Abu_Dhabi | 30 | Josh_Hazlewood | Right | ... | Australia | Aiden_Markram | Right | 40 | 111.10 | SouthAfrica | 119 | Not Achieved | Josh_Hazlewood | Australia |
| Match_2 | England | Windies | Group_stage | England | Fielding | evening | Dubai | 33 | Adil_Rashid | Right | ... | England | Jos_Buttler | Right | 24 | 109.10 | England | 56 | Not Achieved | Moeen_Ali | England |
| Match_3 | Srilanka | Bangladesh | Group_stage | Srilanka | Fielding | afternoon | Sharjah | 34 | Shakib_al_Hassan | Left | ... | Bangladesh | Charith_Asalanka | Left | 80 | 163.20 | Srilanka | 172 | Not Achieved | Charith_Asalanka | Srilanka |
| Match_4 | Pakistan | India | Group_stage | Pakistan | Fielding | evening | Dubai | 34 | Shaheen_shah | Left | ... | Pakistan | Muhammad_Rizwan | Right | 79 | 143.60 | Pakistan | 152 | Not Achieved | Shaheen_shah | Pakistan |
| Match_5 | Afghanistan | Scotland | Group_stage | Afghanistan | Batting | evening | Sharjah | 33 | Mujeeb_ur_Rehman | Right | ... | Afghanistan | Najibullah_Zadran | Left | 59 | 173.53 | Afghanistan | 191 | Achieved | Mujeeb_ur_Rehman | Afghanistan |
5 rows × 23 columns
plt.figure(figsize = (8,6))
sns.countplot(df['stage'], palette = 'Set1')
plt.title("Stage of Tournament")
plt.show()
plt.figure(figsize = (8,6))
sns.countplot(y=df['best_batter_team'], palette = 'Set1')
plt.title("Best Batting Team")
plt.show()
plt.figure(figsize = (8,6))
sns.countplot(y=df['best_bowler_country'], palette = 'Set1')
plt.title("Best Bolwing Team")
plt.show()
print("Top 5 Batsman in T20-World-Cup-2021:")
df['best_batter'].value_counts().nlargest(5)
Top 5 Batsman in T20-World-Cup-2021:
Muhammad_Rizwan 4 Jos_Buttler 3 Daryl_Mitchell 2 David_Warner 2 Najibullah_Zadran 2 Name: best_batter, dtype: int64
print("Top 5 Bolwer in T20-World-Cup-2021:")
df['best_bowler'].value_counts().nlargest(5)
Top 5 Bolwer in T20-World-Cup-2021:
Josh_Hazlewood 3 Trent_Boult 2 Ravindra_Jadeja 2 Shadab_Khan 2 Wanindu_Hasaranga 2 Name: best_bowler, dtype: int64
print("Top 10 highest Score:")
df['target'].sort_values(ascending=False).nlargest(10)
Top 10 highest Score:
Match Number Match_21 211 Match_5 191 Match_19 190 Match_29 190 Match_27 190 Match_23 190 Match_32 177 Match_33 173 Match_20 173 Match_3 172 Name: target, dtype: int64
print("Least 10 Score:")
df['target'].sort_values(ascending=False).nsmallest(10)
Least 10 Score:
Match Number Match_2 56 Match_22 74 Match_18 85 Match_25 86 Match_9 110 Match_16 111 Match_1 119 Match_8 125 Match_28 125 Match_14 126 Name: target, dtype: int64
print("Top 10 high_indvidual_scores:")
df['high_indvidual_scores'].sort_values(ascending=False).nlargest(10)
Top 10 high_indvidual_scores:
Match Number Match_17 101 Match_27 94 Match_20 93 Match_26 89 Match_33 85 Match_23 81 Match_3 80 Match_4 79 Match_19 79 Match_21 74 Name: high_indvidual_scores, dtype: int64
#by each team
plt.figure(figsize = (8,6))
sns.countplot(df['Toss_descision'], palette = 'Set1')
plt.title("Toss descision")
plt.show()
#team 1 & target achievement
achieved_target_team1 = df.groupby(['team_1', 'target_achieved']).size().reset_index(name = 'Count')
#visualize team 1
plt.figure(figsize = (10,6))
chart = sns.barplot(data =achieved_target_team1 , x = 'team_1', y ='Count', hue = 'target_achieved', palette = 'Set1')
chart.set_xticklabels(chart.get_xticklabels(), rotation = 35)
plt.title("Team 1 - Achievement")
plt.show()
#team 2 & target achievement
achieved_target_team2 = df.groupby(['team_2', 'target_achieved']).size().reset_index(name = 'Count')
#visualize team 2
plt.figure(figsize = (10,6))
chart = sns.barplot(data =achieved_target_team2 , x = 'team_2', y ='Count', hue = 'target_achieved', palette = 'Set1')
chart.set_xticklabels(chart.get_xticklabels(), rotation = 35)
plt.title("Team 2 - Achievement")
plt.show()
Team =df.groupby(['team_1','team_2','Winner_toss','time','Toss_descision','venue','Player_of_the_match']).size()
Team=Team.to_frame()
Team
| 0 | |||||||
|---|---|---|---|---|---|---|---|
| team_1 | team_2 | Winner_toss | time | Toss_descision | venue | Player_of_the_match | |
| Afghanistan | Namibia | Afghanistan | afternoon | Batting | Abu_Dhabi | Naveen_ul_Haq | 1 |
| Scotland | Afghanistan | evening | Batting | Sharjah | Mujeeb_ur_Rehman | 1 | |
| Australia | Bangladesh | Australia | afternoon | Fielding | Dubai | Adam_Zampa | 1 |
| New_Zealand | Australia | evening | Fielding | Dubai | Mitchell_Marsh | 1 | |
| Pakistan | Australia | evening | Fielding | Dubai | Matthew_Wade | 1 | |
| SouthAfrica | Australia | afternoon | Fielding | Abu_Dhabi | Josh_Hazlewood | 1 | |
| Srilanka | Australia | evening | Fielding | Dubai | Adam_Zampa | 1 | |
| Windies | Australia | afternoon | Fielding | Abu_Dhabi | David_Warner | 1 | |
| England | Australia | England | evening | Fielding | Dubai | Chris_Jordan | 1 |
| Bangladesh | Bangladesh | afternoon | Batting | Abu_Dhabi | Jason_Roy | 1 | |
| Srilanka | Srilanka | evening | Fielding | Sharjah | Jos_Buttler | 1 | |
| Windies | England | evening | Fielding | Dubai | Moeen_Ali | 1 | |
| India | Afghanistan | Afghanistan | evening | Fielding | Abu_Dhabi | Rohit_Sharma | 1 |
| Namibia | India | evening | Fielding | Dubai | Ravindra_Jadeja | 1 | |
| ScotLand | India | evening | Fielding | Dubai | Ravindra_Jadeja | 1 | |
| Namibia | ScotLand | Namibia | evening | Fielding | Abu_Dhabi | Ruben_Trumpelmann | 1 |
| New_Zealand | Afghanistan | Afghanistan | afternoon | Batting | Abu_Dhabi | Trent_Boult | 1 |
| England | New_Zealand | evening | Fielding | Abu_Dhabi | Daryl_Mitchell | 1 | |
| India | New_Zealand | evening | Fielding | Dubai | Trent_Boult | 1 | |
| Namibia | Namibia | afternoon | Fielding | Sharjah | James_Neesham | 1 | |
| ScotLand | New_Zealand | afternoon | Fielding | Dubai | Martin_Guptill | 1 | |
| Pakistan | Afghanistan | Afghanistan | evening | Batting | Dubai | Asif_Ali | 1 |
| India | Pakistan | evening | Fielding | Dubai | Shaheen_shah | 1 | |
| Namibia | Pakistan | evening | Batting | Abu_Dhabi | Muhammad_Rizwan | 1 | |
| New_Zealand | Pakistan | evening | Fielding | Sharjah | Haris_Rauf | 1 | |
| ScotLand | Pakistan | evening | Batting | Sharjah | Shoaib_Malik | 1 | |
| SouthAfrica | Bangladesh | SouthAfrica | afternoon | Fielding | Abu_Dhabi | Kagiso_Rabada | 1 |
| England | England | evening | Fielding | Sharjah | Rassie_van_der_Dussen | 1 | |
| Srilanka | SouthAfrica | afternoon | Fielding | Sharjah | Tabraiz_Shamsi | 1 | |
| Windies | SouthAfrica | afternoon | Fielding | Dubai | Anrich_Nortje | 1 | |
| Srilanka | Bangladesh | Srilanka | afternoon | Fielding | Sharjah | Charith_Asalanka | 1 |
| Windies | Windies | evening | Fielding | Abu_Dhabi | Chrith_Adalanka | 1 | |
| Windies | Bangladesh | Bangladesh | afternoon | Fielding | Sharjah | Nicholas_Pooran | 1 |
type(Team)
pandas.core.frame.DataFrame
fig = px.sunburst(df, names=None, values=None, parents=None, path=['team_1','team_2','Toss_descision','Winner','venue'],
color='team_2', color_continuous_scale=None, range_color=None, color_continuous_midpoint=None,
color_discrete_sequence=None, color_discrete_map={},
hover_data=['team_1','team_2','Toss_descision','Winner','venue'],
labels={}, title= "Team VS Team - Winner")
fig.show()
Stage=df.groupby(['stage','team_1','team_2','Winner_toss','time','Toss_descision','venue']).size()
Stage=Stage.to_frame()
Stage
| 0 | |||||||
|---|---|---|---|---|---|---|---|
| stage | team_1 | team_2 | Winner_toss | time | Toss_descision | venue | |
| Final | Australia | New_Zealand | Australia | evening | Fielding | Dubai | 1 |
| Group_stage | Afghanistan | Namibia | Afghanistan | afternoon | Batting | Abu_Dhabi | 1 |
| Scotland | Afghanistan | evening | Batting | Sharjah | 1 | ||
| Australia | Bangladesh | Australia | afternoon | Fielding | Dubai | 1 | |
| SouthAfrica | Australia | afternoon | Fielding | Abu_Dhabi | 1 | ||
| Srilanka | Australia | evening | Fielding | Dubai | 1 | ||
| Windies | Australia | afternoon | Fielding | Abu_Dhabi | 1 | ||
| England | Australia | England | evening | Fielding | Dubai | 1 | |
| Bangladesh | Bangladesh | afternoon | Batting | Abu_Dhabi | 1 | ||
| Srilanka | Srilanka | evening | Fielding | Sharjah | 1 | ||
| Windies | England | evening | Fielding | Dubai | 1 | ||
| India | Afghanistan | Afghanistan | evening | Fielding | Abu_Dhabi | 1 | |
| Namibia | India | evening | Fielding | Dubai | 1 | ||
| ScotLand | India | evening | Fielding | Dubai | 1 | ||
| Namibia | ScotLand | Namibia | evening | Fielding | Abu_Dhabi | 1 | |
| New_Zealand | Afghanistan | Afghanistan | afternoon | Batting | Abu_Dhabi | 1 | |
| India | New_Zealand | evening | Fielding | Dubai | 1 | ||
| Namibia | Namibia | afternoon | Fielding | Sharjah | 1 | ||
| ScotLand | New_Zealand | afternoon | Fielding | Dubai | 1 | ||
| Pakistan | Afghanistan | Afghanistan | evening | Batting | Dubai | 1 | |
| India | Pakistan | evening | Fielding | Dubai | 1 | ||
| Namibia | Pakistan | evening | Batting | Abu_Dhabi | 1 | ||
| New_Zealand | Pakistan | evening | Fielding | Sharjah | 1 | ||
| ScotLand | Pakistan | evening | Batting | Sharjah | 1 | ||
| SouthAfrica | Bangladesh | SouthAfrica | afternoon | Fielding | Abu_Dhabi | 1 | |
| England | England | evening | Fielding | Sharjah | 1 | ||
| Srilanka | SouthAfrica | afternoon | Fielding | Sharjah | 1 | ||
| Windies | SouthAfrica | afternoon | Fielding | Dubai | 1 | ||
| Srilanka | Bangladesh | Srilanka | afternoon | Fielding | Sharjah | 1 | |
| Windies | Windies | evening | Fielding | Abu_Dhabi | 1 | ||
| Windies | Bangladesh | Bangladesh | afternoon | Fielding | Sharjah | 1 | |
| Semi_Final | Australia | Pakistan | Australia | evening | Fielding | Dubai | 1 |
| New_Zealand | England | New_Zealand | evening | Fielding | Abu_Dhabi | 1 |
fig = px.sunburst(df, names=None, values=None, parents=None, path=['stage','team_1','team_2','Winner_toss','time','Toss_descision','venue'],
color='team_1', color_continuous_scale=None, range_color=None, color_continuous_midpoint=None,
color_discrete_sequence=None, color_discrete_map={},
hover_data=['stage','team_1','team_2','time','Toss_descision','venue'] ,
labels={'Stage','Team-A','Team-B','Time','Toss_descision','Venue'}, title= "Detailed_Analysis Chart")
fig.show()
#
sns.pairplot(df,hue='team_1')
<seaborn.axisgrid.PairGrid at 0x1faf3567ac0>
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@copyright : Saurabh 29-nov